Approximate computation of multidimensional aggregates of sparse data using wavelets
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Bottom-up computation of sparse and Iceberg CUBE
SIGMOD '99 Proceedings of the 1999 ACM SIGMOD international conference on Management of data
Optimal aggregation algorithms for middleware
PODS '01 Proceedings of the twentieth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Data Cube: A Relational Aggregation Operator Generalizing Group-By, Cross-Tab, and Sub-Totals
Data Mining and Knowledge Discovery
A Microeconomic View of Data Mining
Data Mining and Knowledge Discovery
MM-Cubing: Computing Iceberg Cubes by Factorizing the Lattice Space
SSDBM '04 Proceedings of the 16th International Conference on Scientific and Statistical Database Management
Ordering the attributes of query results
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
DADA: a data cube for dominant relationship analysis
Proceedings of the 2006 ACM SIGMOD international conference on Management of data
Dynamics of bid optimization in online advertisement auctions
Proceedings of the 16th international conference on World Wide Web
Probabilistic ranking of database query results
VLDB '04 Proceedings of the Thirtieth international conference on Very large data bases - Volume 30
Ad-hoc aggregations of ranked lists in the presence of hierarchies
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
ARCube: supporting ranking aggregate queries in partially materialized data cubes
Proceedings of the 2008 ACM SIGMOD international conference on Management of data
A survey of top-k query processing techniques in relational database systems
ACM Computing Surveys (CSUR)
Standing Out in a Crowd: Selecting Attributes for Maximum Visibility
ICDE '08 Proceedings of the 2008 IEEE 24th International Conference on Data Engineering
Subspace Discovery for Promotion: A Cell Clustering Approach
DS '09 Proceedings of the 12th International Conference on Discovery Science
Region-based online promotion analysis
Proceedings of the 13th International Conference on Extending Database Technology
Identifying the most influential data objects with reverse top-k queries
Proceedings of the VLDB Endowment
WINACS: construction and analysis of web-based computer science information networks
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Extracting dimensions for OLAP on multidimensional text databases
WISM'11 Proceedings of the 2011 international conference on Web information systems and mining - Volume Part II
Promotional subspace mining with EProbe framework
Proceedings of the 20th ACM international conference on Information and knowledge management
Discovering the most potential stars in social networks with infra-skyline queries
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
Efficient and domain-invariant competitor mining
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
Efficient influence-based processing of market research queries
Proceedings of the 21st ACM international conference on Information and knowledge management
Discovering influential data objects over time
SSTD'13 Proceedings of the 13th international conference on Advances in Spatial and Temporal Databases
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Promotion is one of the key ingredients in marketing. It is often desirable to find merit in an object (e.g., product, person, organization, or service) and promote it in an appropriate community. In this paper, we propose a novel functionality, called promotion analysis through ranking, for promoting a given object by leveraging highly ranked results. Since the object may not be highly ranked in the global space, our goal is to discover promotive subspaces in which the object becomes prominent. To achieve this goal, the notion of promotiveness is formulated. We show that this functionality is practical and useful in a wide variety of applications such as business intelligence. However, computing promotive subspaces is challenging due to the explosion of search space and high aggregation cost. For efficient computation, we propose a PromoRank framework, and develop three efficient optimization techniques, namely subspace pruning, object pruning, and promotion cube, which are seamlessly integrated into the framework. Our empirical evaluation on two real data sets confirms the effectiveness of promotion analysis, and that our proposed algorithms significantly outperform baseline solutions.